摘要： Previous studies on collaborative filtering have mainly adopted local resources as the basis for conducting analyses, and user rating matrices have been used to perform similarity analysis and prediction. Therefore, the efficiency and correctness of item-based collaborative filtering completely depend on the quantity and comprehensiveness of data collected in a rating matrix. However, data insufficiency leads to the sparsity problem. Additionally, cold-start is an inevitable problem concerning with how local resources are used as the basis for conducting analyses. This paper proposes a new idea by identifying an additional database to support item-based collaborative filtering. Regardless of whether a recommender system operates under a normal condition or applies a sparse matrix and introduces new items, this extra database can be used to accurately calculate item similarity. Moreover, prediction results acquired from two distinctive sets of data can be integrated to enhance the accuracy of the final prediction or successful recommendation. •Previous studies on collaborative filtering have adopted local resources as the basis.•The efficiency of item-based collaborative filtering depends on the quantity of data.•This paper proposes a new idea by identifying an additional database to support item-based collaborative filtering. 出版者： Amsterdam: Elsevier B.V 出版日期： 2016-09 出處： Decision Support Systems, 2016-09, Vol.89, p.17-27 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2016 Elsevier B.V. 版權： Copyright Elsevier Sequoia S.A. Sep 2016 識別號： ISSN: 0167-9236 識別號： EISSN: 1873-5797 識別號： DOI: 10.1016/j.dss.2016.06.005 識別號： CODEN: DSSYDK
